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Record W2282814926 · doi:10.1088/1748-6041/11/1/015014

Selective cell adhesion on femtosecond laser-microstructured polydimethylsiloxane

2016· article· en· W2282814926 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomedical Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversity of Ottawa
FundersOntario Ministry of Economic Development and InnovationUniversity of Ottawa
KeywordsPolydimethylsiloxaneMaterials scienceFemtosecondX-ray photoelectron spectroscopyAdhesionWettingContact angleLaserIrradiationSurface energyLaser ablationCell adhesionNanotechnologyComposite materialChemical engineeringOptics

Abstract

fetched live from OpenAlex

We show that femtosecond laser irradiation of polydimethylsiloxane (PDMS) enables selective and patterned cell growth by altering the wetting properties of the surface associated with chemical and/or topographical changes. In the low pulse energy regime, the surface becomes less hydrophobic and exhibits a low water contact angle compared to the pristine material. X-ray photoelectron spectroscopy (XPS) also reveals an increased oxygen content in the irradiated regions, to which the C2C12 cells and rabbit anti-mouse protein were found to attach preferentially. In the high pulse energy regime, the laser-modified regions exhibit superhydrophobicity and were found to inhibit cell adhesion, whereas cells were found to attach to the surrounding regions due to the presence of nanoscale debris generated by the ablation process.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.211
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it